Systems and methods for non-intrusive deception detection

ABSTRACT

Systems and methods for detecting deceptive intent of a subject include observing eye movements of the subject and correlating the observed movements to known baseline neurophysiological indicators of deception. A detection system may record eye movement data from the subject, compare the eye movement data to a data model comprising threshold eye movement data samples, and from the comparison make a determination whether or not the subject is lying. The detection system may create an alert if deception is detected. The eye movements detected include saccadic and intersaccadic parameters such as intersaccadic drift velocity. Measurements may be collected in situ with a field testing device, such as a non-invasive, non-contact device attached to the subject&#39;s computing device and configured to non-obtrusively record the eye movement data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of, and claims the benefit ofpriority from, U.S. patent application Ser. No. 15/317,339, filed underthe same title on Dec. 8, 2016, which is a National Stage applicationrepresenting entry into the U.S. of International Application No.PCT/US2015/035253, filed under the same title on Jun. 11, 2015, andclaiming the benefit of priority from U.S. Provisional PatentApplication Ser. No. 62/010,658 filed Jun. 11, 2014, all of whichapplications are incorporated by reference herein in their entirety forall purposes.

BACKGROUND OF THE INVENTION

The present disclosure generally relates to systems and methods foracquiring data from a subject and, more particularly, to systems andmethods for gathering and analyzing information about the subject's eyemovements to identify deception by the subject without contacting thesubject.

It is known that practicing lies and deceit provokes physiologicalchanges in the lying subject. Physiological parameters that may deviatefrom normal when a subject is lying include blood pressure, pulse rate,respiration, and skin conductivity. Known “lie detector” technology cantrack these changes. In particular, a polygraph records and measuressome of the changing physiological parameters of the subject while thesubject is asked a series of questions. The underlying understandingabout polygraphs is that deceptive answers are associated withautonomic-controlled physiologic responses of the subject person thatcan readily be differentiated from the physiological responsesassociated with non-deceptive answers.

Polygraphy, also known as a lie detector test, is used as an element ofthe process of providing “security clearance” for highly sensitive andtrusted positions in industry, law enforcement, government, and criticalinfrastructure. Polygraphy is also used extensively in law enforcementas a screening tool to validate statements made by witnesses orsuspects. This form of testing requires the presence of a highlytrained, skilled operator to conduct an examination, perform anevaluation of the results, validate the findings, and report aconclusion. Even though polygraphy is typically inadmissible in a courtof law, many law enforcement agencies believe that the results areaccurate when the test is conducted by expert operators.

Polygraphy's main disadvantage is its reliance on measuring indirectphysiologic responses that are evoked during a deceptive answer to aquestion. This is considered an indirect measure of autonomic responseas opposed to a direct measure of brain activity. It is generallythought that observation of brain function may provide a better means ofidentifying deceptive behavior. Some have suggested making polygraphymore robust by adding additional measurements, such as facialthermography. Others have recommended direct measurements of brainfunction (e.g., EEG) or brain imaging (e.g., PET, MRI), but thesesuggestions have failed to represent practical advancements in thefield. Finally, behavior measures have been suggested. They includedemeanor, use of voice, facial expression, body movements, or choice ofwords. Such measures are subjective and rely upon experienced observers.A non-invasive, non-contact system that measures brain function toidentify lies and deceit is needed.

Such a system could serve to identify lies and deceit even remotely,which would aid in a relatively new environment of dangerous deception:online interactions. The use of social media and other internetapplications—for example, financial transactions and dating sites—haschanged how people must rely upon the truthfulness of interpersonalcommunication. The public is at a far greater risk complicated by aneven greater reliance of an unsuspecting user population. These Internetand web-based applications have virtually eliminated one-to-one contactin which participants can assess the truthfulness of another'sstatement, story, or claim. The visual and auditory clues we typicallyrely on have been replaced by user interfaces that rely on text, email,voicemail, and video communications. Truthful and reliablecommunications are important to users of dating sites and introductoryservices that rely almost entirely on the participants' representations.Likewise financial and other Internet service providers rely upontruthful responses from respondents. These new types of communicationand changes in prior forms of communication expose the parties to thepotential of deception, and the likelihood that one person may takeadvantage of another person's vulnerabilities and trust. A system thatcan protect users of these online services by identifying deception isneeded.

SUMMARY OF THE INVENTION

The present invention overcomes drawbacks of previous technologies byproviding systems and methods that afford a number of advantages andcapabilities not contemplated by, recognized in, or possible intraditional system or known methodologies related to identifying when atest subject is lying or providing deceptive responses.

In one embodiment of the present invention, systems and methods areprovided for monitoring, recording, and/or analyzing eye movements insitu to determine whether oculomotor dynamics are being affected by thesubject's deceptive intent. In one aspect, a sensor arrangement mayinclude a camera and recording assembly for detecting and recording theeye movements.

In some contemplated embodiments, systems and methods using in situtesting of eye movement dynamics may be employed to identify the onsetor presence of abnormal eye movements as a result of the subject lyingor otherwise attempting to deceive. Eye saccades and the velocity ofintersaccadic eye drift are detectably affected by such deceit. A systemand method may alert a user to the deceit in a testing environment. Inparticular, a system in accordance with the present invention mayinclude devices and device assemblies that record baseline data of asubject and generate a data model representing the eye movement data ofthe subject, and further the system may include devices and deviceassemblies that record eye movement data in situ and compare it to thedata model to determine if the subject is lying or being deceitful ordeceptive.

In a contemplated embodiment of the present invention, a system includesa sensing arrangement that collects eye movement data of a subject, anda control unit in communication with the sensing arrangement. Thecontrol unit may be configured to compare the eye movement data to oneor more baseline measurements of eye movement dynamics and, if the eyemovement data diverges from one or more of the baseline measurements bya threshold amount, generate an alert for delivery to the subject.Comparing the eye movement data to the baseline measurements may includecalculating a current intersaccadic drift velocity of the subject andcomparing the current intersaccadic drift velocity to one or morethreshold drift velocities of the baseline measurements. The eyemovement data may include one or more saccade parameters, and comparingthe eye movement data to the baseline measurements may includecalculating a current intersaccadic drift velocity of the subject fromthe saccade parameters and comparing the current intersaccadic driftvelocity to one or more threshold drift velocities of the baselinemeasurements. The baseline measurements may include one or morebio-signatures of a response condition. The response condition may be adeceptive answer or a truthful answer to a question.

In another embodiment of the present invention, a method of determininga physiological state of a subject includes recording from the subjecteye movement data of one or both of the subject's eyes, comparing theeye movement data to one or more baseline measurements, and, if the eyemovement data diverges from one or more of the baseline measurements bya threshold amount, delivering an alert to the subject. The eye movementdata may include one or both of saccade parameters and intersaccadicdrift parameters. The baseline measurements may include one or morebio-signatures of a response condition. The response condition may be adeceptive answer or a truthful answer to a question.

In another embodiment of the present invention, systems and methods ofthe present invention may be combined as a kit or apparatus, whoseadvantages and capabilities will be readily apparent from descriptionsbelow.

The foregoing and other advantages of the invention will appear from thefollowing description. In the description, reference is made to theaccompanying drawings which form a part hereof, and in which there isshown by way of illustration a preferred embodiment of the invention.Such embodiment does not necessarily represent the full scope of theinvention, however, and reference is made therefore to the claims andherein for interpreting the scope of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will hereafter be described with reference to theaccompanying drawings, wherein like reference numerals denote likeelements.

FIG. 1 is a diagram of a detection system in accordance with the presentinvention.

FIG. 2 is a flowchart illustrating a method for detecting lies anddeception in accordance with the present invention.

DETAILED DESCRIPTION

Systems and methods for detecting lies and deception through observationof eye movements are described herein. Deceptive intent or practice isshown by the inventors to affect oculomotor dynamics, including saccadicmetrics and intersaccadic drift metrics. Select oculomotor dynamics canbe tracked against a baseline to alert a subject that the subject islying or attempting to deceive.

The systems and methods described herein are offered for illustrativepurposes only, and are not intended to limit the scope of the presentinvention in any way. Indeed, various modifications of the invention inaddition to those shown and described herein will become apparent tothose skilled in the art from the foregoing description and thefollowing examples and fall within the scope of the appended claims. Forexample, specific disclosure related to lie detection is provided,although it will be appreciated that the systems and methods may beapplied for detection of other mental states without undueexperimentation.

It is proposed herein that patterns of saccadic and micro-saccadicmovement can be identified that are consistent with a subject'spurposeful and deceptive answers to questions, or with other deceptivebehavior. The present systems and methods analyze saccadic andmicro-saccadic eye movements that are not under the control of theindividual. The system eliminates dependence upon recording andmeasuring autonomic and physiologic responses as the primary means ofdetermining whether the test subject and the answer is deceptive.Anatomically, a large portion of the human brain cortex is involved withthe optic system that relies upon eye function for sensory awareness andsurvival. The invention described herein therefore analyzes the functionof the brain more directly than known methods because it identifies,records, and analyzes eye movements that result from interaction betweenthe centers of the brain that are responsible for eye tracking and thoseengaged with emotional and cognitive response.

Eye movement dynamics of the subject are readily identified, recorded,and analyzed by a measuring device to create a useful dataset foranalysis. The dataset enables the comparison of eye movement responsesassociated with additional questions that may or may not be truthful. Asother questions are asked, the eye movements are analyzed and comparedto the baseline set to identify deception. The system performsadditional complex analyses to build a dataset of analyzed eye movementsthat are characterized by their associated truthful or deceptiveanswers. The system thus provides a non-invasive, non-intrusive means toidentify deception during an encounter or interview with a subject thatis not dependent on measuring physiological responses. In particular,the system is ideally suited as a screening tool for internet, socialmedia and other instances where people rely on others to be truthful andsincere.

Using the approach of the present invention, a detection system mayrecord eye movement data from a subject, compare the eye movement datato a data model comprising threshold eye movement data samples, and fromthe comparison make a determination whether or not the subject's brainfunction is altered from a normal state due to the subject's attempt tolie to or deceive one or more individuals. The detection system mayalert the test administrator or another party to the subject's deceit ifan attempt is detected. The detection system does not require extensivetraining to use and is not prone to attempts by the subject to defeatthe testing process by physiological duping.

Regarding the demonstrative eye movements, research has shown thatcertain neurologic conditions, such as diseases that affect the brain,can be identified and differentiated through the analysis of the testsubject's saccadic and micro-saccadic eye movements. Saccadic andmicro-saccadic eye movements are very closely tied to the measurement ofbrain function: As primary sensory organs, the eyes are extensions ofthe optic nerves, which are considered by many to be a part of the brainitself. The characteristic movements have been shown to be veryconsistent, and can be used to develop a bio-signature of a specificdisease or condition. Measuring these eye movements may therefore beconsidered a more direct measurement of brain function than measurementof the physiological responses typically measured in polygraphy.Moreover, these eye movements can be analyzed to identify an attempt todeceive, as described herein.

Referring to FIG. 1, an embodiment of the detection system 10 mayinclude a sensing arrangement 12 configured to detect and record eyemovement dynamics of the subject. The sensing arrangement 12 may includeone or more sensors suitable for collecting the eye movement data. Suchsensors may include a camera or other imaging or motion tracking devicecapable of recording at a suitably high speed and level of detail sothat the subject's eye movement dynamics, including saccades andintersaccadic drift, are captured. A monocular arrangement of one ormore sensors for one of the subject's eyes may be used, or one or moresensors may be included for each eye to obtain binocular data. In someembodiments, the sensors may be miniaturized or otherwise compact,portable, and non-invasive. The sensors may further bevehicle-independent, and may be wireless, to facilitate integration ofthe sensors into any deployment of the detection system 10. For example,the sensing arrangement 12 may include sensors that are integrated intoeyewear, such as on the frame or within the lenses of a pair of glasses.This allows for eye movement data collected even as the subject turnshis head, and allows the sensors to be positioned close to the eyes. Inanother example, the sensors may be integrated into a “lie detector”testing device. In yet another example, the sensors may be integratedinto existing personal devices, such as mobile phones and tabletcomputers. That is, the system 10 may use, as a sensor or array ofsensors, the camera of the personal device in the sensing arrangement12, and may use other native or add-on devices as well. In still anotherexample, the system can be implemented over a social network, using datastorage and processing on a service's servers, as well as laptops,webcams, and other devices of the social network's users as thecomponents of the sensing arrangement 12.

The sensing arrangement 12 may further include integrated or discretedevices for processing, storing, and transmitting collected data. Suchdevices may include a processor, volatile and/or permanent memory, awired or wireless transmitter, and associated power circuits and powersupply for operating the devices. Software modules may define andexecute instructions for operating the sensors, configuring databases,registers, or other data stores, and controlling transmission of thedata. The collected data may be shared via transmission to a controlunit 14 that may be integrated with or disposed physically remotely fromthe sensing arrangement 12. The eye movement data, or a subset thereof,may be transmitted in real-time as it is captured by the sensors, or itmay be stored for later transmission.

The control unit 14 may use the processing hardware (i.e., processor,memory, and the like) of the sensing arrangement 12, or may include itsown processing hardware for analyzing the eye movement data andgenerating an alert to the subject or other party if needed. The controlunit 14 may include a plurality of modules that cooperate to process theeye movement data in a particular fashion, such as according to themethods described below. Each module may include software (or firmware)that, when executed, configures the control unit 14 to perform a desiredfunction. A data analysis module 16 may extract information from the eyemovement data for comparison to the data model. The data analysis module16 may include one or more data filters, such as a Butterworth or othersuitable bandpass filter, that retain only desired signal elements ofthe eye movement data. The data analysis module 16 may include programinstructions for calculating, from the eye movement data, one or moreeye movement dynamics, such as saccades and/or intersaccadic driftvelocities, of the subject's eyes. The calculation may be performedsubstantially in real-time, such that a calculated intersaccadic driftvelocity may be considered the current drift velocity of the subject'seyes.

A comparison module 18 may receive the processed eye movement data fromthe data analysis module 16 and may compare it to the data model asdescribed in detail below. The control unit 14 may include or haveaccess to a model data store 20 that stores the data model. The modeldata store 20 may be a database, data record, register, or othersuitable arrangement for storing data. In some embodiments, the datamodel may simply be a threshold drift velocity, and may thus be storedas a single data record in memory accessible by the comparison module18. In other embodiments, the data model may be a lookup table, linkedlist, array, or other suitable data type depending on the data samplesfor eye movement dynamics or bio-signatures needed to be stored in thedata model.

In some embodiments, the control unit 14 may include a data modelgenerator 22. The data model generator 22 is a module that receives eyemovement data collected by the sensing arrangement 12 during a modelingstep as described below. The data model generator 22 may extract, orcause the data analysis module 16 to extract, information from thecollected eye movement data that will constitute the threshold eyemovement data samples in the data model. The data model generator 22 maythen create the data model from the threshold eye movement data samples,and may store the data model in the data model store 20. In otherembodiments, the data model may be generated and stored in the datamodel store 20 by a separate modeling unit (not shown) of the system 10.The modeling unit may include its own sensing arrangement, processinghardware, and program modules. One suitable modeling unit may be theEyeLink 1000 by SR Research Ltd. of Mississauga, Ontario, Canada.

The control unit 14 may include or communicate with an alertingarrangement 24 configured to produce an alert to the subject, thesubject's conversation partner, a test administrator, or another party,according to the results of the data comparison in the comparison module18. The alerting arrangement may be any suitable indicator andassociated hardware and software for driving the indicator. Suitableindicators present a visual, audible, or otherwise perceptible alert andinclude, without limitation: a visual display such as one or morelight-emitting diodes, a liquid crystal display, a projector, a computeror mobile device screen, and the like; a bell, buzzer, or other audiblesignaling means; and a piezoelectric or other vibrating device. Thealerting arrangement 24 may present the alert in the subject's vicinity,such as when the detection system 10 is used in a test environment.Additionally or alternatively, the alerting arrangement 24 may transmitthe alert to a remote location immediately upon detecting the deceptiveintent or at some later time.

In exemplary embodiments, the detection system 10 can be implemented todetect when a user is lying to a recipient of transmitted data, such asin a recorded video or audio clip, a phone call, a video or text chat,an email or other text-based message, and the like. The detection system10 can alert the recipient to the deception as appropriate for the typeof data transmission. In non-limiting examples, the alerting arrangement24 can produce: during a phone call, an audio alert to the recipient viathe phone when a lie is detected; in an email, a textual or graphicalalert identifying the text being typed by the user when a lie wasdetected; in a video chat, a graphical alert on the recipient's screenwhen a lie is detected; and the like.

The detection system 10 may be used to execute any suitable method ofdetecting a deception by the subject that is indicated by eye movementdata. FIG. 2, illustrates an example method of detecting the deceptionthat can be performed by the system of FIG. 1. At step 100, the systemmay record baseline measurements of the eye movement dynamics for thedata model. The baseline measurements are taken of an individual whichmay or may not be the subject. It may be advantageous that the datamodel use baseline measurements of the subject himself in order toindividualize the operation of the system, but the baseline measurementsmay be taken from individuals other than the subject, or taken from aplurality of subjects and averaged if desired. The conditions in whichthe baseline measurements are recorded may depend on the desiredspecificity of the data model. In some embodiments, the baselinemeasurements may be taken in “normal conditions” by asking the subjectnon-threatening questions that have known answers, such that the testadministrator is certain the subject is telling the truth. In otherembodiments, the baseline measurements may be taken in known “deceptiveconditions,” wherein the subject is asked questions while under mentalfatigue or duress. For example, the subject may be asked extremelydifficult or subjective questions that induce changes in the eyemovement dynamics. The baseline measurements may include eye movementparameters, including saccadic and microsaccadic movement, pupillaryresponse, and eye response to light stimuli. The baseline measurementsmay also include eye measurements not directly related to movements,such as pupil size.

At step 105, the system may calculate one or more threshold driftvelocities from the recorded baseline measurements. The threshold driftvelocities may depend on the format of the collected baselinemeasurements. For example, where only normal-condition or onlydeceptive-condition baseline measurements were taken, a single thresholddrift velocity (i.e., threshold-normal or threshold-deceptive driftvelocity) may be calculated. Calculating the threshold drift velocitiesmay include averaging calculated velocities from all or a portion of theindividuals measured for baseline measurements. Similarly to calculationof drift velocities, any other measured parameter (e.g. pupil size orpapillary response) may be calculated by averaging or normalizing therecorded baseline measurements from multiple individuals. At step 110,the system may generate the data model for the baseline-testedsubject(s). The data model may represent the progression of theintersaccadic drift velocity of the subject from normal conditions todeceptive conditions. The data model may be generated and stored in anysuitable format that allows the system to subsequently compare eyemovement data collected in situ from the subject against the data modelto determine whether the subject is lying.

The data model may include one or more bio-signatures of normal and/ordeceptive responses. A bio-signature is a characteristic pattern thatcan be identified in measurements. This pattern is indicative of a stateof stress or arousal, which may be absent when the subject is truthfuland present when the subject is lying. The pattern may be evident bycomparing the measurements of lying individuals to those of truthfulindividuals, or by comparing the measurements of a particular subjectunder deceptive conditions (i.e., when a response is known to beuntruthful) to the subject's baseline measurements taken under normalconditions. In some embodiments, the bio-signatures may be synthesizedfrom the baseline measurements. The bio-signatures may be general (i.e.,standardized across a population of patients, such as by demographic) orpatient-specific.

The steps 100, 105, 110 for obtaining the data model may be performed atany suitable time before testing the subject in situ for signs ofdeception. In one embodiment, the steps 100-110 may be performed far inadvance and remotely from the test environment. In another embodiment,the steps 100-110 may be performed in the test environment, immediatelypreceding testing the subject. For example, the subject may activate thesystem 10, such as by donning and activating eyewear housing the sensingassembly 12, which initiates step 100 of recording the baselinemeasurements in the present conditions. This may be in normalconditions, wherein the test administrator asks simple questions such as“what is your name?” In still other embodiments, the data model may becreated by the system 10 or another system using a different method thandescribed above.

At step 115, optionally the system may calibrate itself to the subjectif the data model or comparison method require it. For example, the datamodel may be a standardized model generated from baseline measurementsof (an) individual(s) other than the subject, or the comparison methodmay determine the presence of deception from a percentage deviation fromthe subject's threshold-normal drift velocity value(s). In such anembodiment, the system calibrates (step 115) by recording a calibrationset, such as ten seconds or less but preferably five seconds or less, ofeye movement data of the subject when the system is activated in thetest environment under normal conditions. The system may compare thecalibration data to the data model. In one embodiment, this involvesdetermining a deviation of the subject's threshold-normal drift velocityfrom the threshold-normal drift velocity of the model. The system canthen adapt the data model to the subject.

At step 120, the system may record in situ eye movement data from thesubject continuously or at predetermined intervals while the system isactivated. At step 125, the system may calculate, in real-time or atpredetermined intervals, the subject's current drift velocity. At step130, the system may compare the current drift velocity and otherrecorded subject parameters to the data model to determine whether adeviation from or conformance to the data model indicates the subject islying. In one embodiment, when the subject is lying the recorded subjectparameters may deviate from the data model's bio-signature of a truthfulanswer. In another embodiment, when the subject is lying the recordedsubject parameters may conform to the data model's bio-signature of afalse answer. Deviation or conformance may be calculated within anysuitable paradigm. Examples include, without limitation: ratio orpercentage by which the current drift velocity exceeds the subject's orthe data model's threshold-normal drift velocity; ratio or percentage bywhich the current drift velocity is below or above thethreshold-deception drift velocity; comparison of current drift velocityto points on a curve between threshold-normal and threshold-deceptionvalues in the data model; and the like. After the comparison (step 130),the system may return to step 120 and continue recording current data.If the comparison warrants, at step 135 the system may alert a user(e.g., the test administrator) that a lie was detected.

The described system and methods may be implemented in any environmentand during any task that may subject the subject to conditions thataffect eye movements. The various configurations presented above aremerely examples and are in no way meant to limit the scope of thisdisclosure. Variations of the configurations described herein will beapparent to persons of ordinary skill in the art, such variations beingwithin the intended scope of the present application. In particular,features from one or more of the above-described configurations may beselected to create alternative configurations comprised of asub-combination of features that may not be explicitly described above.In addition, features from one or more of the above-describedconfigurations may be selected and combined to create alternativeconfigurations comprised of a combination of features which may not beexplicitly described above. Features suitable for such combinations andsub-combinations would be readily apparent to persons skilled in the artupon review of the present application as a whole. The subject matterdescribed herein and in the recited claims intends to cover and embraceall suitable changes in technology.

1-10. (canceled)
 11. A system for indicating that a subject is lying,the system comprising: a sensing arrangement that collects eye movementdata of the subject; and a control unit in communication with thesensing arrangement, the control unit being configured to: compare acurrent intersaccadic drift velocity of the eye movement data to athreshold drift velocity; and generate an alert when the currentintersaccadic drift velocity is below the threshold drift velocity bymore than a threshold amount, corresponding to an indication that thesubject is lying.
 12. The system of claim 11, wherein the control unitis further configured to calculate the current intersaccadic driftvelocity of the subject from the eye movement data.
 13. (canceled) 14.(canceled)
 15. The system of claim 11, and further comprising comparingthe eye movement data to one or more baseline measurements, wherein theone or more baseline measurements comprise one or more bio-signatureseach corresponding to one of one or more response conditions.
 16. Thesystem of claim 15, wherein one of the one or more response conditionsis a deceptive answer to a question. 17-20. (canceled)
 21. A system fordetecting deceptive intent of a user, the system comprising: a sensingarrangement that collects eye movement data of a user; an alertingarrangement that produces a perceptible alert in response to receipt ofan alert signal; and a control unit in electronic communication with thesensing arrangement and the alerting arrangement, the control unitconfigured to: extract current eye movement dynamics from the eyemovement data, the current eye movement dynamics including a currentintersaccadic drift velocity of the user, comparing the current eyemovement dynamics to baseline eye movement dynamics, the baseline eyemovement dynamics including a threshold drift velocity, identify adeceptive intent of the user when the current intersaccadic driftvelocity is below the threshold drift velocity by more than a thresholdamount, and send the alert signal to the alerting arrangement inresponse to an identification of the deceptive intent.
 22. The system ofclaim 21, wherein the sensing arrangement includes one of a camera and amotion tracking device.
 23. The system of claim 21, wherein the sensingarrangement comprises one or more sensors in electronic communicationwith a computing device of the user.
 24. The system of claim 23, whereinthe control unit is in electronic communication with the computingdevice and the alerting arrangement is in electronic communication witha recipient device operated by a recipient of data transmitted by theuser, the alerting arrangement being configured to produce the alert onthe recipient device.
 25. The system of claim 24, wherein the datatransmitted by the user is transmitted over a social network to whichthe computing device of the user and the recipient device are connected.26. The system of claim 21, wherein the control unit is furtherconfigured to calculate the current intersaccadic drift velocity. 27.The system of claim 21, wherein the sensing arrangement comprisessensors incorporated into one of a mobile phone or a tablet.
 28. Thesystem of claim 21, wherein control unit is further configured tocalculate the threshold drift velocity based on previous eye movementdata of the user collected during a calibration process.
 29. The systemof claim 21, wherein the control unit is further configured to send aremote alert signal to a device remote from the user.
 30. A method ofdetermining whether a subject is lying, the method comprising: obtainingeye movement data of one or both of the subject's eyes; identifying acurrent intersaccadic drift velocity from the eye movement data;comparing, with a detection device, the current intersaccadic driftvelocity of the eye movement data to a threshold drift velocity; andwhen the comparison indicates that the current intersaccadic driftvelocity is below the threshold drift velocity by more than a thresholdamount, corresponding to an indication that the subject is lying,delivering, with the detection device, an alert to a device associatedwith one or more of the subject and an administrator.
 31. The method ofclaim 30, wherein the threshold drift velocity is part of one or morebio-signatures each corresponding to one of one or more responseconditions.
 32. The method of claim 31, wherein one of the one or moreresponse conditions is a deceptive answer to a question.
 33. The methodof claim 30 and further comprising: obtaining baseline eye movement dataduring one of a known deceptive condition or a known non-deceptivecondition; and calculating the threshold drift velocity from thebaseline eye movement data.
 34. The method of claim 33, wherein thebaseline eye movement data is obtained from an individual other than thesubject.
 35. The method of claim 30 and further comprising comparing theeye movement data to a bio-signature comprising a pattern indicative ofa state of stress; and when the comparison indicates that thebio-signature is present in the eye movement data, corresponding to anindication that the subject is lying, delivering, with the detectiondevice, the alert to a device associated with one or more of the subjectand an administrator.
 36. The method of claim 30, wherein identifyingthe current intersaccadic drift velocity from the eye movement dataincludes identifying the current intersaccadic drift velocity when thesubject is responding to a question.